Transformer state detection and assessment method based on voiceprint compression and cost-sensitive techniques

Abstract

Voiceprint detection technology can assist inspectors in assessing the state of transformers. A method for detecting and assessing transformer states based on voiceprint compression and cost-sensitive techniques is proposed. The method first extracts voiceprint features from transformer audio, then filters and compresses these features in the frequency domain. Subsequently, a convolutional neural network is employed to evaluate the transformer′s state, incorporating a cost-sensitive loss function to enhance attention towards difficult samples. Using a 35 kV transformer as the experimental subject, transformer audio data is collected through on-site recordings, simulated experiments and sample augmentation. Test results demonstrate that the proposed method reduces the voiceprint dimensionality from 1 025 to 80, decreasing computational complexity and video memory usage to 8.1% and 7.7% of the original 1 025 dimensions, respectively. Simultaneously, the proposed method achieves a voiceprint recognition accuracy of 83.5% and improves the recall rate of the most challenging short-circuit current anomaly from 48.2% to 63.6%.

Keywords

• transformer detection
• acoustic pattern recognition
• acoustic pattern compression
• cost sensitivity
• convolutional neural networks
• pattern recognition